<html><head><title>The Fabric Data</title>
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<table width="100%"><tr><td>fabric(gamlss.util)</td><td align="right">R Documentation</td></tr></table><object type="application/x-oleobject" classid="clsid:1e2a7bd0-dab9-11d0-b93a-00c04fc99f9e">
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<h2>The Fabric Data</h2>


<h3>Description</h3>

<p>
The data are 32 observations on faults in rolls of fabric
</p>


<h3>Usage</h3>

<pre>data(fabric)</pre>


<h3>Format</h3>

<p>
A data frame with 32 observations on the following 3 variables.
<dl>
<dt>leng</dt><dd>the length of the roll : a  numeric vector</dd>
<dt>y</dt><dd>the number of faults in the roll of fabric  : a  discrete vector</dd>
<dt>x</dt><dd>the log of the length of the roll : a  numeric vector</dd>
</dl>

<h3>Details</h3>

<p>
The data are 32 observations on faults in rolls of fabric taken from Hinde (1982) 
who used the EM algorithm to fit a Poisson-normal model. 
The response variable is the number of faults in the roll of fabric and the explanatory variable is the log of the length of the roll.
</p>


<h3>Source</h3>

<p>
John Hinde
</p>


<h3>References</h3>

<p>
Hinde,  J. (1982) Compound Poisson regression models: in <EM>GLIM</EM> 82, <EM>Proceedings of the
International Conference on Generalized Linear Models</EM>, ed. Gilchrist, R., 109&ndash;121, Springer: New York.
</p>


<h3>Examples</h3>

<pre>
data(fabric)
attach(fabric)
plot(x,y)
detach(fabric)
</pre>



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